BSAGraphs
2024-05-09
df <- read_csv("./data/combinedstatshot.csv")## Rows: 165 Columns: 36
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (12): player, dunk_tot, dunk_pct, rim_tot, rim_pct, rim_asted, other2pt_...
## dbl (24): games, games_started, mp_per_g, fg_per_g, fga_per_g, fg_pct, fg2_p...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.df <- df |> select(!c("games_started", "pf_per_g"))
colnames(df)## [1] "player" "dunk_tot" "dunk_pct" "rim_tot"
## [5] "rim_pct" "rim_asted" "other2pt_tot" "other2pt_pct"
## [9] "other2pt_asted" "3pt_tot" "3pt_pct" "3pt_asted"
## [13] "games" "mp_per_g" "fg_per_g" "fga_per_g"
## [17] "fg_pct" "fg2_per_g" "fg2a_per_g" "fg2_pct"
## [21] "fg3_per_g" "fg3a_per_g" "fg3_pct" "ft_per_g"
## [25] "fta_per_g" "ft_pct" "orb_per_g" "drb_per_g"
## [29] "trb_per_g" "ast_per_g" "stl_per_g" "blk_per_g"
## [33] "tov_per_g" "pts_per_g"Plot 2pt attempts to 3pt attempts
From success script
path <- "~/BruinSports/data/draftdata.csv"
df_career_stats <- read_csv(path)## Rows: 960 Columns: 23
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): team_id, player, college_name, skip
## dbl (19): pick_overall, seasons, g, mp, pts, trb, ast, fg_pct, fg3_pct, ft_p...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.drop_cols <- c('team_id', 'skip', 'mp', 'pts', 'trb', 'ast')
df_career_stats <- df_career_stats |> select(!drop_cols)## Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
## # Was:
## data %>% select(drop_cols)
##
## # Now:
## data %>% select(all_of(drop_cols))
##
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.df_lot_picks <- df_career_stats |> filter(pick_overall < 15)
colnames(df_career_stats)## [1] "pick_overall" "player" "college_name" "seasons" "g"
## [6] "fg_pct" "fg3_pct" "ft_pct" "mp_per_g" "pts_per_g"
## [11] "trb_per_g" "ast_per_g" "ws" "ws_per_48" "bpm"
## [16] "vorp" "year"# plotting the distribution of points + rebounds + asts (PRA) per game
df_lot_picks <- df_lot_picks |> mutate(
pra_per_g = pts_per_g + trb_per_g + ast_per_g,
pick_overall = factor(pick_overall)
)
# Group by draft position, and getting the mean
draft_means <- df_lot_picks |> group_by(pick_overall) |>
summarize(avg_mpg = mean(mp_per_g),
avg_ppg = mean(pts_per_g),
avg_trbpg = mean(trb_per_g),
avg_apg = mean(ast_per_g),
avg_prapg = mean(pra_per_g))
print(draft_means, n = 14)## # A tibble: 14 × 6
## pick_overall avg_mpg avg_ppg avg_trbpg avg_apg avg_prapg
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 31.2 18.8 6.52 4.32 29.7
## 2 2 26.9 13.7 4.96 2.88 21.6
## 3 3 30.0 17.6 6.3 3.52 27.4
## 4 4 27.5 12.8 5.39 2.48 20.7
## 5 5 26.0 12.7 4.92 3.55 21.2
## 6 6 23.2 10.2 4.35 2.26 16.8
## 7 7 27.6 13.4 4.88 2.77 21.0
## 8 8 21.1 8.82 3.37 1.77 14.0
## 9 9 24.5 10.9 4.55 2.52 18.0
## 10 10 23.5 10.4 3.61 2.31 16.3
## 11 11 21.1 10.2 3.69 2.16 16.0
## 12 12 24.6 10.6 4.47 2.23 17.3
## 13 13 22.6 10.9 3.99 2.23 17.1
## 14 14 20.3 8.78 3.75 1.3 13.8df_lot_picks |> ggplot(aes(x = pick_overall, y = pra_per_g)) +
geom_boxplot() +
labs(x = "Draft Pick", y = "Points-Rebounds-Assists Per Game")
Plotting the players in the top 25% in PRA per game
df2 <- left_join(df, df_lot_picks, by = c("player"), suffix = c("_college", "_nba"))
df2 <- df2 |>
separate_wider_delim(dunk_tot, delim = "-", names = c("dunk_made", "dunk_attempts"))
df2 <- df2 |>
separate_wider_delim(rim_tot, delim = "-", names = c("rim_made", "rim_attempts"))
df2 <- df2 |>
separate_wider_delim(other2pt_tot, delim = "-", names = c("other2pt_made", "other2pt_attempts"))
df2 <-df2 |>
mutate(across(c(dunk_made, dunk_attempts, rim_made, rim_attempts, other2pt_made, other2pt_attempts), as.numeric))defining bust metric
df2 <- df2 |> mutate(
vorp_per_g = vorp / g
)
df_top_players <- df2 |>
group_by(pick_overall) |>
summarize(across(c(pts_per_g_nba, trb_per_g_nba, ast_per_g_nba, pra_per_g, vorp_per_g), function(x) quantile(x, probs = 0.7)))
df_bottom_players <- df2 |>
group_by(pick_overall) |>
summarize(across(c(pts_per_g_nba, trb_per_g_nba, ast_per_g_nba, pra_per_g, vorp_per_g), function(x) quantile(x, probs = 0.3)))
# metric favors big men
# make the rebound percentile higher
is_not_bust <- function(pick_number, df_top_players = df_top_players, df2 = df2) {
ppg <- df_top_players |> pull(pts_per_g_nba)
apg <- df_top_players |> pull(ast_per_g_nba)
prapg <- df_top_players |> pull(pra_per_g)
df_top_rb <- df2 |>
group_by(pick_overall) |>
summarize(trb_per_g = quantile(trb_per_g_nba, probs = 0.8))
rpg <- df_top_rb |> pull(trb_per_g)
df_top_vorp <- df2 |>
group_by(pick_overall) |>
summarize(vorp_per_g = median(vorp_per_g))
vorppg <- df_top_vorp |> pull(vorp_per_g)
df <- df2 |> filter(pick_overall == pick_number) |>
filter(pra_per_g >= prapg[[pick_number]] | ast_per_g_nba >= apg[[pick_number]] | trb_per_g_nba >= rpg[[pick_number]] | pts_per_g_nba >= ppg[[pick_number]]) |>
filter(vorp_per_g >= vorppg[[pick_number]])
# must also have played at least most of their career in the nba
df <- df |> filter(seasons >= 4 / 5 * (2023-year))
return(df)
}
is_bust <- function(pick_number, df_bottom_players = df_bottom_players, df2 = df2) {
ppg <- df_bottom_players |> pull(pts_per_g_nba)
apg <- df_bottom_players |> pull(ast_per_g_nba)
prapg <- df_bottom_players |> pull(pra_per_g)
df_bottom_rb <- df2 |>
group_by(pick_overall) |>
summarize(trb_per_g = quantile(trb_per_g_nba, probs = 0.4))
rpg <- df_bottom_rb |> pull(trb_per_g)
df_top_vorp <- df2 |>
group_by(pick_overall) |>
summarize(vorp_per_g = median(vorp_per_g))
vorppg <- df_top_vorp |> pull(vorp_per_g)
# playing less than half the seasons since drafted makes you a bust
df <- df2 |> filter(pick_overall == pick_number) |>
filter((pra_per_g < prapg[[pick_number]] & ast_per_g_nba < apg[[pick_number]] & trb_per_g_nba < rpg[[pick_number]] & pts_per_g_nba < ppg[[pick_number]] & vorp_per_g < vorppg[[pick_number]]) | seasons < 1 / 2 * (2023-year))
return(df)
}# first overall picks (not bust)
df_pick_1 <- is_not_bust(1, df_top_players, df2)
# first overall picks (bust)
df_pick_1_bust <- is_bust(1, df_bottom_players, df2)
df_pick_1## # A tibble: 7 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 John Wall 33 36 91.7% 116 182 63.7%
## 2 Kyrie Irving 0 0 0% 26 39 66.7%
## 3 Anthony Davis 96 98 98.0% 152 174 87.4%
## 4 Karl-Anthony T… 22 24 91.7% 87 121 71.9%
## 5 Ben Simmons 56 61 91.8% 159 220 72.3%
## 6 Zion Williamson 72 79 91.1% 247 313 78.9%
## 7 Anthony Edwards 27 27 100.0% 89 129 69.0%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …df_pick_1_bust## # A tibble: 1 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Anthony Bennett 53 58 91.4% 100 140 71.4%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …# second overall
df_pick_2 <- is_not_bust(2, df_top_players, df2)
df_pick_2_bust <- is_bust(2, df_bottom_players, df2)
df_pick_2## # A tibble: 5 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 D'Angelo Russe… 4 4 100.0% 70 110 63.6%
## 2 Brandon Ingram 17 17 100.0% 69 117 59.0%
## 3 Lonzo Ball 37 40 92.5% 94 120 78.3%
## 4 Ja Morant 28 31 90.3% 160 264 60.6%
## 5 Chet Holmgren 57 57 100.0% 105 125 84.0%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …df_pick_2_bust## # A tibble: 1 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Derrick Willia… 56 60 93.3% 135 188 71.8%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …# third overall
df_pick_3 <- is_not_bust(3, df_top_players, df2)
df_pick_3_bust <- is_bust(3, df_bottom_players, df2)
df_pick_3## # A tibble: 4 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Bradley Beal 18 20 90.0% 89 137 65.0%
## 2 Joel Embiid 30 30 100.0% 80 99 80.8%
## 3 Jayson Tatum 18 21 85.7% 79 126 62.7%
## 4 Evan Mobley 63 66 95.5% 113 144 78.5%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …df_pick_3_bust## # A tibble: 1 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Jahlil Okafor 64 67 95.5% 213 270 78.9%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …# fourth overall
df_pick_4 <- is_not_bust(4, df_top_players, df2)
df_pick_4_bust <- is_bust(4, df_bottom_players, df2)
df_pick_4## # A tibble: 4 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Aaron Gordon 54 56 96.4% 137 198 69.2%
## 2 Jaren Jackson … 31 31 100.0% 61 93 65.6%
## 3 Scottie Barnes 19 21 90.5% 61 89 68.5%
## 4 Keegan Murray 63 67 94.0% 196 277 70.8%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …df_pick_4_bust## # A tibble: 0 × 55
## # ℹ 55 variables: player <chr>, dunk_made <dbl>, dunk_attempts <dbl>,
## # dunk_pct <chr>, rim_made <dbl>, rim_attempts <dbl>, rim_pct <chr>,
## # rim_asted <chr>, other2pt_made <dbl>, other2pt_attempts <dbl>,
## # other2pt_pct <chr>, other2pt_asted <chr>, 3pt_tot <chr>, 3pt_pct <chr>,
## # 3pt_asted <chr>, games <dbl>, mp_per_g_college <dbl>, fg_per_g <dbl>,
## # fga_per_g <dbl>, fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>,
## # fg2_pct <dbl>, fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, …# fifth overall
df_pick_5 <- is_not_bust(5, df_top_players, df2)
df_pick_5_bust <- is_bust(5, df_bottom_players, df2)
df_pick_5## # A tibble: 3 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 DeMarcus Cousi… 53 57 93.0% 144 189 76.2%
## 2 De'Aaron Fox 20 21 95.2% 131 203 64.5%
## 3 Trae Young 0 0 0% 105 201 52.2%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …df_pick_5_bust## # A tibble: 1 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Thomas Robinson 70 83 84.3% 169 262 64.5%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …# sixth overall
df_pick_6 <- is_not_bust(6, df_top_players, df2)
df_pick_6_bust <- is_bust(6, df_bottom_players, df2)
df_pick_6## # A tibble: 5 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Damian Lillard 13 17 76.5% 98 169 58.0%
## 2 Nerlens Noel 48 50 96.0% 76 99 76.8%
## 3 Marcus Smart 16 18 88.9% 78 110 70.9%
## 4 Buddy Hield 18 22 81.8% 119 178 66.9%
## 5 Onyeka Okongwu 58 61 95.1% 135 186 72.6%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …df_pick_6_bust## # A tibble: 1 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Ekpe Udoh 30 32 93.8% 78 109 71.6%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …# seventh overall
df_pick_7 <- is_not_bust(7, df_top_players, df2)
df_pick_7_bust <- is_bust(7, df_bottom_players, df2)
df_pick_7## # A tibble: 3 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Julius Randle 37 40 92.5% 132 197 67.0%
## 2 Jamal Murray 18 19 94.7% 77 111 69.4%
## 3 Lauri Markkanen 20 24 83.3% 65 100 65.0%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …df_pick_7_bust## # A tibble: 1 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Ben McLemore 44 45 97.8% 90 126 71.4%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …# eighth overall
df_pick_8 <- is_not_bust(8, df_top_players, df2)
df_pick_8_bust <- is_bust(8, df_bottom_players, df2)
df_pick_8## # A tibble: 3 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Al-Farouq Aminu 46 48 95.8% 112 173 64.7%
## 2 Kentavious Cal… 15 16 93.8% 63 94 67.0%
## 3 Franz Wagner 11 11 100.0% 63 93 67.7%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …df_pick_8_bust## # A tibble: 0 × 55
## # ℹ 55 variables: player <chr>, dunk_made <dbl>, dunk_attempts <dbl>,
## # dunk_pct <chr>, rim_made <dbl>, rim_attempts <dbl>, rim_pct <chr>,
## # rim_asted <chr>, other2pt_made <dbl>, other2pt_attempts <dbl>,
## # other2pt_pct <chr>, other2pt_asted <chr>, 3pt_tot <chr>, 3pt_pct <chr>,
## # 3pt_asted <chr>, games <dbl>, mp_per_g_college <dbl>, fg_per_g <dbl>,
## # fga_per_g <dbl>, fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>,
## # fg2_pct <dbl>, fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, …# ninth overall
df_pick_9 <- is_not_bust(9, df_top_players, df2)
df_pick_9_bust <- is_bust(9, df_bottom_players, df2)
df_pick_9## # A tibble: 5 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Gordon Hayward 19 20 95.0% 89 128 69.5%
## 2 Kemba Walker 3 3 100.0% 115 196 58.7%
## 3 Andre Drummond 80 89 89.9% 130 185 70.3%
## 4 Trey Burke 9 9 100.0% 67 105 63.8%
## 5 Jakob Poeltl 32 34 94.1% 199 284 70.1%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …df_pick_9_bust## # A tibble: 1 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Kevin Knox 18 20 90.0% 65 99 65.7%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …# tenth overall
df_pick_10 <- is_not_bust(10, df_top_players, df2)
df_pick_10_bust <- is_bust(10, df_bottom_players, df2)
df_pick_10## # A tibble: 5 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Paul George 18 22 81.8% 70 106 66.0%
## 2 CJ McCollum 3 3 100.0% 34 63 54.0%
## 3 Elfrid Payton 21 24 87.5% 169 247 68.4%
## 4 Mikal Bridges 35 42 83.3% 109 161 67.7%
## 5 Jalen Smith 49 52 94.2% 114 158 72.2%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …df_pick_10_bust## # A tibble: 2 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Ziaire Williams 10 11 90.9% 26 49 53.1%
## 2 Johnny Davis 16 19 84.2% 89 143 62.2%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …# eleventh overall
df_pick_11 <- is_not_bust(11, df_top_players, df2)
df_pick_11_bust <- is_bust(11, df_bottom_players, df2)
df_pick_11## # A tibble: 4 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Klay Thompson 8 8 100.0% 66 110 60.0%
## 2 Myles Turner 11 13 84.6% 40 54 74.1%
## 3 Domantas Sabon… 22 24 91.7% 157 214 73.4%
## 4 Shai Gilgeous-… 11 11 100.0% 108 182 59.3%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …df_pick_11_bust## # A tibble: 2 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 James Bouknight 12 12 100.0% 52 79 65.8%
## 2 Jett Howard 6 6 100.0% 29 47 61.7%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …# twelveth overall
df_pick_12 <- is_not_bust(12, df_top_players, df2)
df_pick_12_bust <- is_bust(12, df_bottom_players, df2)
df_pick_12## # A tibble: 5 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Steven Adams 29 33 87.9% 85 129 65.9%
## 2 Miles Bridges 30 35 85.7% 84 128 65.6%
## 3 Tyrese Halibur… 7 8 87.5% 46 62 74.2%
## 4 Jalen Williams 25 27 92.6% 124 186 66.7%
## 5 Dereck Lively … 54 55 98.2% 74 96 77.1%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …df_pick_12_bust## # A tibble: 1 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Xavier Henry 17 17 100.0% 60 90 66.7%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …# thirteenth overall
df_pick_13 <- is_not_bust(13, df_top_players, df2)
df_pick_13_bust <- is_bust(13, df_bottom_players, df2)
df_pick_13## # A tibble: 7 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Ed Davis 26 27 96.3% 42 50 84.0%
## 2 Kelly Olynyk 25 28 89.3% 152 212 71.7%
## 3 Zach LaVine 21 25 84.0% 51 90 56.7%
## 4 Devin Booker 8 9 88.9% 42 59 71.2%
## 5 Donovan Mitche… 9 13 69.2% 64 116 55.2%
## 6 Tyler Herro 4 5 80.0% 56 84 66.7%
## 7 Jalen Duren 70 76 92.1% 111 152 73.0%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …df_pick_13_bust## # A tibble: 2 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Kendall Marsha… 0 0 0% 35 53 66.0%
## 2 Jerome Robinson 12 13 92.3% 98 157 62.4%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …# fourteenth overall
df_pick_14 <- is_not_bust(14, df_top_players, df2)
df_pick_14_bust <- is_bust(14, df_bottom_players, df2)
df_pick_14## # A tibble: 4 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Marcus Morris 31 33 93.9% 114 147 77.6%
## 2 T.J. Warren 37 37 100.0% 192 251 76.5%
## 3 Cameron Payne 3 3 100.0% 53 87 60.9%
## 4 Bam Adebayo 99 105 94.3% 138 185 74.6%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …df_pick_14_bust## # A tibble: 1 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Romeo Langford 10 14 71.4% 91 138 65.9%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …# combining dataframes
df_good <- bind_rows(df_pick_1, df_pick_2, df_pick_3, df_pick_4, df_pick_5, df_pick_6, df_pick_7, df_pick_8, df_pick_9,
df_pick_10, df_pick_11, df_pick_12, df_pick_13, df_pick_14)
df_busts <- bind_rows(df_pick_1_bust, df_pick_2_bust, df_pick_3_bust, df_pick_4_bust, df_pick_5_bust, df_pick_6_bust, df_pick_7_bust, df_pick_8_bust, df_pick_9_bust, df_pick_10_bust, df_pick_11_bust, df_pick_12_bust, df_pick_13_bust, df_pick_14_bust)
print(df_good, n = 20)## # A tibble: 64 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 John Wall 33 36 91.7% 116 182 63.7%
## 2 Kyrie Irving 0 0 0% 26 39 66.7%
## 3 Anthony Davis 96 98 98.0% 152 174 87.4%
## 4 Karl-Anthony … 22 24 91.7% 87 121 71.9%
## 5 Ben Simmons 56 61 91.8% 159 220 72.3%
## 6 Zion Williams… 72 79 91.1% 247 313 78.9%
## 7 Anthony Edwar… 27 27 100.0% 89 129 69.0%
## 8 D'Angelo Russ… 4 4 100.0% 70 110 63.6%
## 9 Brandon Ingram 17 17 100.0% 69 117 59.0%
## 10 Lonzo Ball 37 40 92.5% 94 120 78.3%
## 11 Ja Morant 28 31 90.3% 160 264 60.6%
## 12 Chet Holmgren 57 57 100.0% 105 125 84.0%
## 13 Bradley Beal 18 20 90.0% 89 137 65.0%
## 14 Joel Embiid 30 30 100.0% 80 99 80.8%
## 15 Jayson Tatum 18 21 85.7% 79 126 62.7%
## 16 Evan Mobley 63 66 95.5% 113 144 78.5%
## 17 Aaron Gordon 54 56 96.4% 137 198 69.2%
## 18 Jaren Jackson… 31 31 100.0% 61 93 65.6%
## 19 Scottie Barnes 19 21 90.5% 61 89 68.5%
## 20 Keegan Murray 63 67 94.0% 196 277 70.8%
## # ℹ 44 more rows
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>, …df_good |> pull(player)## [1] "John Wall" "Kyrie Irving"
## [3] "Anthony Davis" "Karl-Anthony Towns"
## [5] "Ben Simmons" "Zion Williamson"
## [7] "Anthony Edwards" "D'Angelo Russell"
## [9] "Brandon Ingram" "Lonzo Ball"
## [11] "Ja Morant" "Chet Holmgren"
## [13] "Bradley Beal" "Joel Embiid"
## [15] "Jayson Tatum" "Evan Mobley"
## [17] "Aaron Gordon" "Jaren Jackson Jr."
## [19] "Scottie Barnes" "Keegan Murray"
## [21] "DeMarcus Cousins" "De'Aaron Fox"
## [23] "Trae Young" "Damian Lillard"
## [25] "Nerlens Noel" "Marcus Smart"
## [27] "Buddy Hield" "Onyeka Okongwu"
## [29] "Julius Randle" "Jamal Murray"
## [31] "Lauri Markkanen" "Al-Farouq Aminu"
## [33] "Kentavious Caldwell-Pope" "Franz Wagner"
## [35] "Gordon Hayward" "Kemba Walker"
## [37] "Andre Drummond" "Trey Burke"
## [39] "Jakob Poeltl" "Paul George"
## [41] "CJ McCollum" "Elfrid Payton"
## [43] "Mikal Bridges" "Jalen Smith"
## [45] "Klay Thompson" "Myles Turner"
## [47] "Domantas Sabonis" "Shai Gilgeous-Alexander"
## [49] "Steven Adams" "Miles Bridges"
## [51] "Tyrese Haliburton" "Jalen Williams"
## [53] "Dereck Lively II" "Ed Davis"
## [55] "Kelly Olynyk" "Zach LaVine"
## [57] "Devin Booker" "Donovan Mitchell"
## [59] "Tyler Herro" "Jalen Duren"
## [61] "Marcus Morris" "T.J. Warren"
## [63] "Cameron Payne" "Bam Adebayo"df_busts## # A tibble: 15 × 55
## player dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 Anthony Benne… 53 58 91.4% 100 140 71.4%
## 2 Derrick Willi… 56 60 93.3% 135 188 71.8%
## 3 Jahlil Okafor 64 67 95.5% 213 270 78.9%
## 4 Thomas Robins… 70 83 84.3% 169 262 64.5%
## 5 Ekpe Udoh 30 32 93.8% 78 109 71.6%
## 6 Ben McLemore 44 45 97.8% 90 126 71.4%
## 7 Kevin Knox 18 20 90.0% 65 99 65.7%
## 8 Ziaire Willia… 10 11 90.9% 26 49 53.1%
## 9 Johnny Davis 16 19 84.2% 89 143 62.2%
## 10 James Bouknig… 12 12 100.0% 52 79 65.8%
## 11 Jett Howard 6 6 100.0% 29 47 61.7%
## 12 Xavier Henry 17 17 100.0% 60 90 66.7%
## 13 Kendall Marsh… 0 0 0% 35 53 66.0%
## 14 Jerome Robins… 12 13 92.3% 98 157 62.4%
## 15 Romeo Langford 10 14 71.4% 91 138 65.9%
## # ℹ 48 more variables: rim_asted <chr>, other2pt_made <dbl>,
## # other2pt_attempts <dbl>, other2pt_pct <chr>, other2pt_asted <chr>,
## # `3pt_tot` <chr>, `3pt_pct` <chr>, `3pt_asted` <chr>, games <dbl>,
## # mp_per_g_college <dbl>, fg_per_g <dbl>, fga_per_g <dbl>,
## # fg_pct_college <dbl>, fg2_per_g <dbl>, fg2a_per_g <dbl>, fg2_pct <dbl>,
## # fg3_per_g <dbl>, fg3a_per_g <dbl>, fg3_pct_college <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ft_pct_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>, …df_busts |> pull(player)## [1] "Anthony Bennett" "Derrick Williams" "Jahlil Okafor" "Thomas Robinson"
## [5] "Ekpe Udoh" "Ben McLemore" "Kevin Knox" "Ziaire Williams"
## [9] "Johnny Davis" "James Bouknight" "Jett Howard" "Xavier Henry"
## [13] "Kendall Marshall" "Jerome Robinson" "Romeo Langford"plot_good <- ggplot(df_good, aes(x = fg2_per_g, y = fg3_per_g)) +
geom_point(color = "green", size = 4, alpha = 0.5) +
geom_label_repel(data = df_good, aes(label = player), size = 1.5, max.overlaps = 20) +
labs(title = "CBB Shot Selection for Good NBA Lottery Picks", x = "2PT Field Goal Makes per game", y = "3PT Field Goal Makes per game") +
theme_bw()
plot_busts <- ggplot(df_busts, aes(x = fg2_per_g, y = fg3_per_g, label = player)) +
geom_point(color = "red", size = 4, alpha = 0.5) +
geom_label_repel(size = 3) +
labs(title = "CBB Shot Selection for NBA Lottery Busts", x = "2PT Field Goal Makes per game", y = "3PT Field Goal Makes per game") +
theme_bw()
plot_combined <- ggplot(df2, aes(x = fg2_per_g, y = fg3_per_g)) +
geom_point(aes(color = "Average value"), size = 4, alpha = 0.2) +
geom_point(data = df_busts, aes(color = "Bad value"), size = 4, alpha = 0.5) +
geom_point(data = df_good, aes(color = "Good value"), size = 4, alpha = 0.5) +
labs(title = "CBB Shot Selection for NBA Lottery Picks",
x = "2PT Field Goal Makes per game", y = "3PT Field Goal Makes per game") +
scale_color_manual(values = c("Bad value" = "red", "Good value" = "green", "Average value" = "grey")) +
theme_bw()
plot_combined
plot_busts
plot_good
Principal Component Analysis of college stats, for grouping/covariance
library(corrr)
library(ggcorrplot)
library(FactoMineR)
library(factoextra)## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBaPreparing Data
colnames(df2)## [1] "player" "dunk_made" "dunk_attempts"
## [4] "dunk_pct" "rim_made" "rim_attempts"
## [7] "rim_pct" "rim_asted" "other2pt_made"
## [10] "other2pt_attempts" "other2pt_pct" "other2pt_asted"
## [13] "3pt_tot" "3pt_pct" "3pt_asted"
## [16] "games" "mp_per_g_college" "fg_per_g"
## [19] "fga_per_g" "fg_pct_college" "fg2_per_g"
## [22] "fg2a_per_g" "fg2_pct" "fg3_per_g"
## [25] "fg3a_per_g" "fg3_pct_college" "ft_per_g"
## [28] "fta_per_g" "ft_pct_college" "orb_per_g"
## [31] "drb_per_g" "trb_per_g_college" "ast_per_g_college"
## [34] "stl_per_g" "blk_per_g" "tov_per_g"
## [37] "pts_per_g_college" "pick_overall" "college_name"
## [40] "seasons" "g" "fg_pct_nba"
## [43] "fg3_pct_nba" "ft_pct_nba" "mp_per_g_nba"
## [46] "pts_per_g_nba" "trb_per_g_nba" "ast_per_g_nba"
## [49] "ws" "ws_per_48" "bpm"
## [52] "vorp" "year" "pra_per_g"
## [55] "vorp_per_g"df3 <- column_to_rownames(df2, var = "player")
df_cbb <- df3 |> select(
dunk_made, dunk_attempts, dunk_pct, rim_made, rim_attempts, rim_pct, rim_asted,
other2pt_made, other2pt_attempts, other2pt_pct, other2pt_asted, fg2_pct, fg3_per_g, fg3a_per_g,
`3pt_asted`, games, ft_per_g, fta_per_g, ast_per_g_college, orb_per_g, drb_per_g,
stl_per_g, blk_per_g, tov_per_g, pts_per_g_college
) |> rename(fg3_asted = `3pt_asted`) |>
mutate(dunk_pct = parse_number(dunk_pct) / 100, # changing to percentages
rim_pct = parse_number(rim_pct) / 100,
rim_asted = parse_number(rim_asted) / 100,
other2pt_pct = parse_number(other2pt_pct) / 100,
other2pt_asted = parse_number(other2pt_asted) / 100,
fg3_asted = parse_number(fg3_asted) / 100,
fg3_pct_per_g = fg3_per_g / fg3a_per_g)
df_cbb <- df_cbb |>
mutate(fg3_pct_per_g = coalesce(fg3_pct_per_g, 0)) |>
relocate(fg3_pct_per_g, .after = fg3_asted)
to_per_game <- function(x, games) {
x <- x / games
return(x)
}
df_cbb <- df_cbb |> # making everything per game
mutate(across(c(dunk_made, dunk_attempts, rim_made, rim_attempts, other2pt_made,
other2pt_attempts), function(x) to_per_game(x, games)))
colSums(is.na(df_cbb))## dunk_made dunk_attempts dunk_pct rim_made
## 0 0 0 0
## rim_attempts rim_pct rim_asted other2pt_made
## 0 0 0 0
## other2pt_attempts other2pt_pct other2pt_asted fg2_pct
## 0 0 0 0
## fg3_per_g fg3a_per_g fg3_asted fg3_pct_per_g
## 0 0 0 0
## games ft_per_g fta_per_g ast_per_g_college
## 0 0 0 0
## orb_per_g drb_per_g stl_per_g blk_per_g
## 0 0 0 0
## tov_per_g pts_per_g_college
## 0 0Following this guide: https://www.datacamp.com/tutorial/pca-analysis-r
df_cbb_scaled <- as_tibble(scale(df_cbb |> select(-games)))
df_cbb_scaled## # A tibble: 165 × 25
## dunk_made dunk_attempts dunk_pct rim_made rim_attempts rim_pct rim_asted
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.360 0.381 0.147 0.898 1.16 -0.569 -0.927
## 2 -0.777 -0.801 0.334 -0.908 -1.09 1.49 -1.46
## 3 1.28 1.26 0.328 1.47 1.12 1.46 0.671
## 4 0.623 0.680 0.0540 0.492 0.395 0.600 0.742
## 5 1.16 1.17 0.228 1.45 1.19 1.17 0.642
## 6 0.267 0.254 0.278 0.0749 -0.0119 0.531 -0.0603
## 7 -0.546 -0.575 0.384 0.247 0.380 -0.430 0.0517
## 8 0.121 0.0815 0.403 -0.231 -0.159 -0.430 0.0753
## 9 -0.622 -0.648 0.353 -0.670 -0.737 0.238 0.624
## 10 -0.606 -0.559 -0.469 -0.822 -0.844 -0.249 -0.615
## # ℹ 155 more rows
## # ℹ 18 more variables: other2pt_made <dbl>, other2pt_attempts <dbl>,
## # other2pt_pct <dbl>, other2pt_asted <dbl>, fg2_pct <dbl>, fg3_per_g <dbl>,
## # fg3a_per_g <dbl>, fg3_asted <dbl>, fg3_pct_per_g <dbl>, ft_per_g <dbl>,
## # fta_per_g <dbl>, ast_per_g_college <dbl>, orb_per_g <dbl>, drb_per_g <dbl>,
## # stl_per_g <dbl>, blk_per_g <dbl>, tov_per_g <dbl>, pts_per_g_college <dbl># df_cbb_scaled$player <- df_cbb$player
# df_cbb_scaled <- df_cbb_scaled |> relocate(player, .before = dunk_made)
corr_matrix <- cor(df_cbb_scaled)
ggcorrplot(corr_matrix, method = "square")
ggcorrplot(corr_matrix, method = "square", hc.order = TRUE,
type = "lower")
pca <- princomp(corr_matrix)
summary(pca)## Importance of components:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
## Standard deviation 1.5264968 0.8275209 0.38297252 0.26189097 0.2392287
## Proportion of Variance 0.6731171 0.1978139 0.04236762 0.01981253 0.0165320
## Cumulative Proportion 0.6731171 0.8709310 0.91329860 0.93311112 0.9496431
## Comp.6 Comp.7 Comp.8 Comp.9 Comp.10
## Standard deviation 0.2338592 0.19672875 0.166119165 0.136620686 0.092515541
## Proportion of Variance 0.0157982 0.01117981 0.007971468 0.005391775 0.002472454
## Cumulative Proportion 0.9654413 0.97662114 0.984592603 0.989984378 0.992456832
## Comp.11 Comp.12 Comp.13 Comp.14
## Standard deviation 0.087727419 0.077813416 0.063038666 0.0522112215
## Proportion of Variance 0.002223154 0.001749072 0.001147923 0.0007874564
## Cumulative Proportion 0.994679986 0.996429058 0.997576981 0.9983644376
## Comp.15 Comp.16 Comp.17 Comp.18
## Standard deviation 0.0466045703 0.0419416771 0.0315945772 0.0227293361
## Proportion of Variance 0.0006274163 0.0005081482 0.0002883526 0.0001492356
## Cumulative Proportion 0.9989918540 0.9995000022 0.9997883548 0.9999375904
## Comp.19 Comp.20 Comp.21 Comp.22
## Standard deviation 1.371363e-02 4.393920e-03 2.341039e-03 1.590896e-03
## Proportion of Variance 5.432546e-05 5.577032e-06 1.583129e-06 7.311092e-07
## Cumulative Proportion 9.999919e-01 9.999975e-01 9.999991e-01 9.999998e-01
## Comp.23 Comp.24 Comp.25
## Standard deviation 7.221375e-04 3.825110e-04 7.167572e-09
## Proportion of Variance 1.506394e-07 4.226557e-08 1.484031e-17
## Cumulative Proportion 1.000000e+00 1.000000e+00 1.000000e+00fviz_eig(pca, addlabels = TRUE, title = "Principal Components Scree Plot")
fviz_pca_var(pca, col.var = "cos2", repel = TRUE, title = "Contributions of Variables to Components 1 and 2", gradient.cols = c("lightblue", "black"))
fviz_cos2(pca, choice = "var", axes = 1:2)
Kmeans Clustering: https://medium.com/@zullinira23/implementation-of-principal-component-analysis-pca-on-k-means-clustering-in-r-794f03ec15f
df_cbb.sample <- df_cbb |> sample_frac(1, replace = FALSE)
head(df_cbb.sample)## dunk_made dunk_attempts dunk_pct rim_made rim_attempts
## Myles Turner 0.32352941 0.38235294 0.846 1.1764706 1.588235
## Jimmer Fredette 0.01438849 0.02158273 0.667 0.7841727 1.086331
## Collin Sexton 0.15151515 0.24242424 0.625 3.1212121 5.515152
## John Henson 0.50458716 0.52293578 0.965 0.7614679 1.055046
## Kelly Olynyk 0.24752475 0.27722772 0.893 1.5049505 2.099010
## Trey Lyles 0.77777778 0.83333333 0.933 1.9166667 2.444444
## rim_pct rim_asted other2pt_made other2pt_attempts other2pt_pct
## Myles Turner 0.741 0.650 1.7647059 4.1470588 0.426
## Jimmer Fredette 0.722 0.294 0.8057554 2.1510791 0.375
## Collin Sexton 0.566 0.233 1.4848485 3.7575758 0.395
## John Henson 0.722 0.759 1.1192661 2.7064220 0.414
## Kelly Olynyk 0.717 0.553 0.4752475 0.9009901 0.527
## Trey Lyles 0.784 0.565 1.2222222 3.4166667 0.358
## other2pt_asted fg2_pct fg3_per_g fg3a_per_g fg3_asted
## Myles Turner 0.633 0.513 0.5 1.8 0.765
## Jimmer Fredette 0.089 0.497 2.1 5.4 0.455
## Collin Sexton 0.061 0.495 1.3 4.0 0.318
## John Henson 0.697 0.505 0.0 0.2 0.000
## Kelly Olynyk 0.417 0.634 0.2 0.7 0.889
## Trey Lyles 0.614 0.536 0.1 0.8 0.750
## fg3_pct_per_g games ft_per_g fta_per_g ast_per_g_college
## Myles Turner 0.2777778 34 2.8 3.3 0.6
## Jimmer Fredette 0.3888889 139 4.5 5.1 3.7
## Collin Sexton 0.3250000 33 5.9 7.6 3.6
## John Henson 0.0000000 109 1.6 3.2 1.0
## Kelly Olynyk 0.2857143 101 2.0 2.8 1.1
## Trey Lyles 0.1250000 36 2.1 2.8 1.1
## orb_per_g drb_per_g stl_per_g blk_per_g tov_per_g
## Myles Turner 1.3 5.2 0.3 2.6 1.4
## Jimmer Fredette 0.5 2.1 1.2 0.1 2.5
## Collin Sexton 1.0 2.7 0.8 0.1 2.8
## John Henson 2.5 5.7 0.6 2.5 1.5
## Kelly Olynyk 1.5 3.1 0.5 0.5 1.4
## Trey Lyles 1.8 3.4 0.5 0.4 1.1
## pts_per_g_college
## Myles Turner 10.1
## Jimmer Fredette 18.7
## Collin Sexton 19.2
## John Henson 10.3
## Kelly Olynyk 8.9
## Trey Lyles 8.7df_cbb.pca <- PCA(df_cbb.sample |> select(-games), scale.unit = TRUE, graph = FALSE)
fviz_eig(df_cbb.pca, addlabels = TRUE)
fviz_pca_var(df_cbb.pca, col.var = "cos2", gradient.cols = c("lightblue", "black"), repel = TRUE)
var <- get_pca_var(df_cbb.pca)
fviz_cos2(df_cbb.pca, choice = "var", axes = 1:2)
fviz_pca_ind(df_cbb.pca, repel = TRUE, labelsize = 1)
summary(df_cbb.pca)##
## Call:
## PCA(X = select(df_cbb.sample, -games), scale.unit = TRUE, graph = FALSE)
##
##
## Eigenvalues
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6 Dim.7
## Variance 7.910 5.334 2.090 1.463 1.278 1.196 1.058
## % of var. 31.642 21.336 8.359 5.852 5.113 4.784 4.231
## Cumulative % of var. 31.642 52.978 61.336 67.189 72.302 77.085 81.316
## Dim.8 Dim.9 Dim.10 Dim.11 Dim.12 Dim.13 Dim.14
## Variance 0.980 0.728 0.551 0.447 0.419 0.332 0.269
## % of var. 3.922 2.911 2.203 1.787 1.677 1.329 1.075
## Cumulative % of var. 85.238 88.149 90.352 92.140 93.817 95.146 96.221
## Dim.15 Dim.16 Dim.17 Dim.18 Dim.19 Dim.20 Dim.21
## Variance 0.254 0.210 0.164 0.138 0.072 0.062 0.022
## % of var. 1.017 0.840 0.655 0.553 0.286 0.247 0.088
## Cumulative % of var. 97.238 98.079 98.734 99.287 99.574 99.821 99.909
## Dim.22 Dim.23 Dim.24 Dim.25
## Variance 0.012 0.006 0.004 0.002
## % of var. 0.046 0.023 0.014 0.007
## Cumulative % of var. 99.955 99.978 99.993 100.000
##
## Individuals (the 10 first)
## Dist Dim.1 ctr cos2 Dim.2 ctr cos2
## Myles Turner | 4.749 | 1.929 0.285 0.165 | -1.917 0.418 0.163 |
## Jimmer Fredette | 4.809 | -3.867 1.146 0.647 | 0.263 0.008 0.003 |
## Collin Sexton | 5.851 | -2.729 0.570 0.217 | 3.754 1.601 0.412 |
## John Henson | 6.154 | 3.308 0.838 0.289 | -2.665 0.807 0.188 |
## Kelly Olynyk | 4.781 | 1.070 0.088 0.050 | -3.551 1.433 0.552 |
## Trey Lyles | 4.437 | 2.347 0.422 0.280 | -2.411 0.660 0.295 |
## Anthony Black | 3.631 | -1.285 0.126 0.125 | 1.571 0.280 0.187 |
## Thomas Robinson | 5.982 | 0.703 0.038 0.014 | -2.124 0.512 0.126 |
## Kris Dunn | 4.707 | -2.539 0.494 0.291 | 0.121 0.002 0.001 |
## Otto Porter Jr. | 3.375 | 0.208 0.003 0.004 | -1.729 0.340 0.263 |
## Dim.3 ctr cos2
## Myles Turner 2.091 1.268 0.194 |
## Jimmer Fredette -0.758 0.167 0.025 |
## Collin Sexton -0.753 0.165 0.017 |
## John Henson -0.650 0.123 0.011 |
## Kelly Olynyk 0.137 0.005 0.001 |
## Trey Lyles 0.456 0.060 0.011 |
## Anthony Black -2.131 1.317 0.345 |
## Thomas Robinson 1.739 0.877 0.085 |
## Kris Dunn -2.822 2.310 0.359 |
## Otto Porter Jr. 0.974 0.275 0.083 |
##
## Variables (the 10 first)
## Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3 ctr
## dunk_made | 0.834 8.788 0.695 | 0.336 2.113 0.113 | -0.023 0.025
## dunk_attempts | 0.828 8.662 0.685 | 0.337 2.132 0.114 | -0.023 0.025
## dunk_pct | 0.343 1.489 0.118 | -0.070 0.091 0.005 | 0.214 2.193
## rim_made | 0.585 4.330 0.343 | 0.673 8.503 0.454 | -0.093 0.416
## rim_attempts | 0.453 2.598 0.205 | 0.740 10.256 0.547 | -0.097 0.448
## rim_pct | 0.728 6.706 0.530 | -0.141 0.373 0.020 | -0.002 0.000
## rim_asted | 0.768 7.456 0.590 | -0.352 2.327 0.124 | 0.180 1.558
## other2pt_made | 0.109 0.149 0.012 | 0.603 6.823 0.364 | 0.563 15.195
## other2pt_attempts | 0.102 0.132 0.010 | 0.651 7.952 0.424 | 0.490 11.501
## other2pt_pct | 0.015 0.003 0.000 | -0.039 0.029 0.002 | 0.358 6.122
## cos2
## dunk_made 0.001 |
## dunk_attempts 0.001 |
## dunk_pct 0.046 |
## rim_made 0.009 |
## rim_attempts 0.009 |
## rim_pct 0.000 |
## rim_asted 0.033 |
## other2pt_made 0.318 |
## other2pt_attempts 0.240 |
## other2pt_pct 0.128 |pca2 <- prcomp(df_cbb.sample, center = TRUE, scale = TRUE)
summary(pca2)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 2.8233 2.3564 1.45379 1.28788 1.18415 1.09891 1.03423
## Proportion of Variance 0.3066 0.2135 0.08129 0.06379 0.05393 0.04645 0.04114
## Cumulative Proportion 0.3066 0.5201 0.60142 0.66521 0.71914 0.76559 0.80673
## PC8 PC9 PC10 PC11 PC12 PC13 PC14
## Standard deviation 0.99030 0.85552 0.75400 0.67049 0.65151 0.64104 0.57399
## Proportion of Variance 0.03772 0.02815 0.02187 0.01729 0.01633 0.01581 0.01267
## Cumulative Proportion 0.84445 0.87260 0.89447 0.91176 0.92808 0.94389 0.95656
## PC15 PC16 PC17 PC18 PC19 PC20 PC21
## Standard deviation 0.51590 0.49111 0.40960 0.40167 0.37014 0.26727 0.20134
## Proportion of Variance 0.01024 0.00928 0.00645 0.00621 0.00527 0.00275 0.00156
## Cumulative Proportion 0.96680 0.97607 0.98253 0.98873 0.99400 0.99675 0.99831
## PC22 PC23 PC24 PC25 PC26
## Standard deviation 0.14559 0.10740 0.07652 0.06000 0.04282
## Proportion of Variance 0.00082 0.00044 0.00023 0.00014 0.00007
## Cumulative Proportion 0.99912 0.99957 0.99979 0.99993 1.00000df_cluster <- as.data.frame(-pca2$x[, 1:2])
df_cluster## PC1 PC2
## Myles Turner -1.91803535 -1.800563503
## Jimmer Fredette 4.10068583 -0.151455721
## Collin Sexton 2.49870233 3.953433538
## John Henson -3.00626308 -3.180396627
## Kelly Olynyk -0.78055387 -3.858127881
## Trey Lyles -2.31079038 -2.311364621
## Anthony Black 1.15287196 1.737863300
## Thomas Robinson -0.46560196 -2.442928178
## Kris Dunn 2.64831964 -0.054392871
## Otto Porter Jr. -0.11050188 -1.773914232
## Shabazz Muhammad -0.41381159 2.159011494
## Tyrese Haliburton 2.20631834 -2.697991218
## Deandre Ayton -6.42063201 3.989709140
## Meyers Leonard -2.79244626 -3.646226533
## Cade Cunningham 2.85426045 4.449564443
## Kentavious Caldwell-Pope 2.20137247 -0.823482753
## Paul George 2.04066776 -0.155321706
## Marvin Bagley III -5.74422617 4.591555606
## Cameron Payne 4.43284878 1.397687085
## Kendall Marshall 4.50594244 -1.991570055
## Anthony Bennett -3.16583380 1.008475683
## Jakob Poeltl -3.09307807 -1.010869663
## Austin Rivers 2.94309828 1.408832101
## Jalen Suggs 1.51193273 1.416722416
## Taurean Prince 1.41666106 -3.713851811
## Trae Young 6.57771252 7.880998042
## De'Aaron Fox 0.82524429 3.344308266
## Victor Oladipo 0.59336620 -2.543388232
## Jarace Walker -1.37801187 -1.216966913
## Onyeka Okongwu -5.85709792 2.599723240
## Zion Williamson -5.17455606 4.695617011
## DeMarcus Cousins -4.47722765 2.532951574
## Andre Drummond -6.01362530 -0.579810217
## Elfrid Payton 1.97980456 0.903624480
## Mo Bamba -5.19088002 0.667639378
## Donovan Mitchell 3.10251794 -1.824734500
## Alex Len -3.65666018 -2.221479147
## Kevin Knox 0.57857116 0.787030146
## Wendell Carter Jr. -3.43851631 0.759548294
## Marquese Chriss -2.85951816 0.454169608
## Davion Mitchell 3.46328985 -1.590099336
## De'Andre Hunter 0.73977851 -2.045387990
## Cameron Johnson 1.40190265 -2.112733416
## Ekpe Udoh -2.95349083 2.108737125
## Al-Farouq Aminu -1.06979323 0.683184259
## Anthony Davis -6.81946695 1.298925467
## Aaron Gordon -2.77173027 0.539925608
## Derrick Favors -5.07612783 0.537442192
## Jaxson Hayes -6.14378844 -1.935755954
## Evan Mobley -4.34455690 2.691116412
## Jaren Jackson Jr. -1.54989689 -1.654063788
## Cam Reddish 4.08549065 -0.100918560
## Jordan Hawkins 3.10079731 -2.711257786
## Stanley Johnson 1.12015696 1.170691584
## Nik Stauskas 3.10769654 -1.847553292
## Joshua Primo 1.51844668 -3.740079647
## Justise Winslow 0.32450165 0.046720801
## Doug McDermott 0.97886742 -1.321678910
## Julius Randle -2.24340507 3.042945784
## Jaylen Brown 0.83240004 2.294511909
## Jaden Ivey 1.84082641 -0.056522486
## Brandon Knight 3.48956878 2.033951063
## Romeo Langford 0.69108021 2.226529738
## RJ Barrett 0.01227205 5.110094647
## Patrick Williams 0.07429544 -1.354172663
## Cole Aldrich -2.80512022 -3.923034303
## Jonathan Isaac -1.55579355 -0.429327692
## Josh Jackson -1.57310640 3.071593787
## James Bouknight 1.50440966 -0.805096400
## Damian Lillard 4.26753179 0.405236246
## Kira Lewis Jr. 3.22512852 0.145447070
## Marcus Smart 2.54328344 2.015237976
## Michael Carter-Williams 2.86581601 -0.870790993
## Shai Gilgeous-Alexander 1.99930055 2.359093850
## Cason Wallace 2.27311780 -0.159975101
## Kemba Walker 3.16087458 0.642457328
## Anthony Edwards 1.36140263 2.673735875
## Taylor Hendricks -1.22550512 -0.175647086
## Ochai Agbaji 1.61424758 -3.117399223
## Jalen Williams 1.77088011 -1.662712781
## Karl-Anthony Towns -2.12840084 -1.109330782
## Xavier Henry 1.19039770 -0.836284356
## Lonzo Ball 0.20427619 0.082601232
## D'Angelo Russell 2.92106171 2.705140788
## Jeremy Sochan -0.94012964 -1.962196272
## Harrison Barnes 1.07392023 -0.415393391
## Lauri Markkanen 0.13400874 -0.115337390
## Trey Burke 3.56814702 0.621441435
## T.J. Warren -1.14870807 0.419904979
## Ja Morant 2.44350644 3.154068779
## Buddy Hield 3.50692348 -1.416444649
## Scottie Barnes 0.34079523 0.018038969
## Greg Monroe -0.61040396 0.650297864
## Cody Zeller -2.04375199 0.560514715
## Patrick Patterson -2.59675241 -2.071578251
## Joel Embiid -4.56071760 -0.275049579
## Noah Vonleh -1.16775866 0.188969620
## Dennis Smith Jr. 2.38981682 4.058571848
## Michael Kidd-Gilchrist -1.54339794 0.537228608
## Bennedict Mathurin 1.03736333 -1.047637079
## Terrence Ross 1.12336118 -2.567881207
## Brandon Ingram 1.61026723 1.916191506
## Ed Davis -3.34988242 -3.186735688
## Jalen Smith -2.16080003 -1.786709370
## P.J. Washington -0.38430532 -1.057924680
## Jamal Murray 2.32101496 1.773731573
## Jett Howard 2.88030411 -1.519978771
## John Wall 1.68160821 3.975501776
## Coby White 3.30501478 0.962609638
## Evan Turner 1.44633779 0.509352088
## Paolo Banchero -0.78117820 2.804053329
## Dereck Lively II -5.13940729 -4.908354365
## Devin Vassell 0.68178092 -4.075928100
## Keegan Murray -1.65497692 -0.671771098
## Marcus Morris -0.78619850 -2.586158338
## Derrick Williams -1.06807992 1.073410109
## Markieff Morris -1.34708852 -3.999247075
## Jerome Robinson 3.01145976 -0.204910540
## Jalen Duren -6.04834658 0.567839906
## Aaron Nesmith 3.24624148 -1.628776546
## Gradey Dick 1.38567770 -1.245442843
## Ziaire Williams 1.73956057 -0.236909097
## Bradley Beal 0.82754947 0.659943331
## Jahlil Okafor -5.53352532 2.780697880
## CJ McCollum 3.87520407 0.619831203
## Obi Toppin -3.10013537 -0.850875977
## Frank Kaminsky 0.17667352 -4.085571358
## Kyrie Irving 3.69754992 2.860691262
## Zach Collins -2.29766335 -2.206084028
## Jayson Tatum 0.72039348 2.664410902
## Jabari Smith Jr. 1.57911124 1.495506781
## Franz Wagner 1.19544539 -2.167513895
## Chris Duarte 1.86046277 -1.292904206
## Malik Monk 2.24035661 1.712986893
## Dion Waiters 1.87877434 -2.893422978
## Zach LaVine 1.55093364 -2.642329927
## Bam Adebayo -6.03442637 1.151787352
## Mikal Bridges 1.14420389 -3.554794904
## Alec Burks 0.72391129 2.047902304
## Jarrett Culver 1.47235138 -0.024087543
## Ben McLemore -0.30783252 0.245872619
## Jabari Parker -2.47210802 3.877981214
## Jeremy Lamb 0.44879947 -2.590888791
## Willie Cauley-Stein -2.67767592 -4.026223965
## Brandon Miller 1.94836905 2.026234708
## Klay Thompson 3.79667554 -0.207371725
## Andrew Wiggins -0.40394987 2.619085299
## Rui Hachimura -0.93351176 -2.636481966
## Nerlens Noel -5.52149230 0.801447386
## Isaac Okoro -0.45583166 0.276546204
## Miles Bridges 0.43665877 -0.849012232
## Devin Booker 1.24174959 -3.484009154
## Luke Kennard 2.73577444 -0.919701787
## Wes Johnson -1.59432891 1.355658714
## Gordon Hayward 0.63712837 -0.809277017
## Tyler Herro 1.75186786 -0.416344735
## Steven Adams -3.71212322 -2.800032293
## Markelle Fultz 1.98389283 6.289069268
## Tristan Thompson 4.06381690 -2.262810456
## Johnny Davis 1.29838193 -0.145475806
## Denzel Valentine 3.02166326 -3.193070266
## Chet Holmgren -4.24371197 0.005887613
## Domantas Sabonis -1.73763383 -1.176518418
## Ben Simmons -3.63357550 6.421547748
## Moses Moody 1.43065318 1.445225669fviz_nbclust(df_cluster, kmeans, method = 'wss')
fviz_nbclust(df_cluster, kmeans, method = 'silhouette')
fviz_nbclust(df_cluster, kmeans, method = 'gap_stat')
k <- 6
df_cbb.kmeans = kmeans(df_cluster, centers = k, nstart = 50)
fviz_cluster(df_cbb.kmeans, df_cluster, labelsize = 5, repel = TRUE)